Last updated: 2020-08-05

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LGA level summary

df_mesh_lockdown_summary  %>%
  group_by(lga_name) %>%
  select(-starts_with('mc'), -State) %>%
  mutate( area = units::set_units( area, 'km^2') ) %>%
  summarise(across(.cols=where(is_numeric),
                   .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum),
                   .names = "{col}.{fn}"), number_mesh_blocks = n(),
            .groups='drop') %>%
  { . } -> df_lga_summary
Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated
df_lga_summary %>%
  gt::gt()
lga_name area.mean area.max area.min area.sd area.total AREA_ALBERS_SQKM.mean AREA_ALBERS_SQKM.max AREA_ALBERS_SQKM.min AREA_ALBERS_SQKM.sd AREA_ALBERS_SQKM.total Dwelling.mean Dwelling.max Dwelling.min Dwelling.sd Dwelling.total Person.mean Person.max Person.min Person.sd Person.total number_mesh_blocks
Banyule 15.168065 [km^2] 18.05298 [km^2] 7.873072e+00 [km^2] 1.5629573 23161.635 [km^2] 0.04011742 1.3652 0.0036 0.06027309 61.2593 32.43811 137 0 18.00586 49533 78.75966 250 0 45.51041 120266 1527
Bayside 6.165186 [km^2] 10.68724 [km^2] 3.338694e+00 [km^2] 1.6371322 7755.804 [km^2] 0.02929515 0.7697 0.0003 0.03758751 36.8533 32.41017 370 0 22.79189 40772 76.53657 564 0 49.77356 96283 1258
Boroondara 11.215679 [km^2] 17.42271 [km^2] 6.842769e+00 [km^2] 2.8686374 24708.141 [km^2] 0.02697758 0.5884 0.0012 0.02620521 59.4316 31.13709 312 0 22.84866 68595 74.94281 530 0 50.50130 165099 2203
Brimbank 10.702852 [km^2] 14.63235 [km^2] 3.923643e+00 [km^2] 2.3460959 23792.439 [km^2] 0.05504584 2.8094 0.0013 0.13988827 122.3669 30.85695 213 0 20.90102 68595 86.48088 377 0 58.12934 192247 2223
Cardinia 3.775535 [km^2] 61.46820 [km^2] 1.422561e-02 [km^2] 5.3788539 4005.843 [km^2] 0.94752036 57.7477 0.0026 3.38631748 1005.3191 31.41942 331 0 22.61667 33336 84.71536 520 0 58.37599 89883 1061
Casey 9.188315 [km^2] 28.49109 [km^2] 1.404326e-02 [km^2] 5.1832254 27656.828 [km^2] 0.13318306 13.5396 0.0035 0.67467536 400.8810 33.52625 584 0 23.19496 100914 98.39103 1563 0 64.94951 296157 3010
Darebin 11.183692 [km^2] 16.27913 [km^2] 8.394428e+00 [km^2] 2.0861874 21181.913 [km^2] 0.02807049 2.3848 0.0011 0.07237729 53.1655 32.93083 214 0 20.46158 62371 76.17740 910 0 50.89965 144280 1894
Frankston 11.736426 [km^2] 16.12092 [km^2] 3.484195e+00 [km^2] 2.6215798 21994.062 [km^2] 0.06844616 9.6412 0.0015 0.28167073 128.2681 30.04269 431 0 21.80828 56300 70.89007 693 0 50.20077 132848 1874
Glen Eira 7.823174 [km^2] 11.77597 [km^2] 5.105705e+00 [km^2] 1.5036070 13268.102 [km^2] 0.02262388 0.7053 0.0023 0.02337052 38.3701 34.95932 303 0 17.86337 59291 82.23762 295 0 40.49210 139475 1696
Greater Dandenong 13.344279 [km^2] 25.13646 [km^2] 2.202326e+00 [km^2] 4.3734979 24460.063 [km^2] 0.07025145 7.4485 0.0019 0.35374179 128.7709 29.41571 377 0 22.26615 53919 81.65521 524 0 57.03544 149674 1833
Hobsons Bay 10.594129 [km^2] 19.48648 [km^2] 4.060148e+00 [km^2] 4.4794391 13136.720 [km^2] 0.05149129 4.3148 0.0024 0.18092210 63.8492 29.66129 163 0 21.80291 36780 70.69597 391 0 52.38760 87663 1240
Hume 9.574864 [km^2] 20.41986 [km^2] 8.181058e-04 [km^2] 5.2964617 19475.273 [km^2] 0.19683024 33.0496 0.0015 1.31093473 400.3527 32.60914 309 0 22.40951 66327 95.58997 830 0 66.23920 194430 2034
Kingston 10.585755 [km^2] 15.24164 [km^2] 7.060328e+00 [km^2] 1.8726474 23193.390 [km^2] 0.04144099 3.6878 0.0008 0.13469082 90.7972 28.90552 222 0 20.75976 63332 68.55454 369 0 47.97661 150203 2191
Knox 16.076124 [km^2] 30.23447 [km^2] 6.897432e+00 [km^2] 5.7438726 27458.020 [km^2] 0.06604596 4.9027 0.0031 0.20228764 112.8065 34.28806 216 0 22.05790 58564 89.19087 418 0 54.11022 152338 1708
Manningham 12.974971 [km^2] 18.87721 [km^2] 3.985259e+00 [km^2] 3.9468991 17866.536 [km^2] 0.08092905 3.4244 0.0024 0.19927045 111.4393 32.54829 333 0 21.64656 44819 83.29484 442 0 48.68907 114697 1377
Maribyrnong 9.261396 [km^2] 11.67654 [km^2] 7.598767e+00 [km^2] 0.8137522 10002.308 [km^2] 0.02874787 1.3674 0.0012 0.06533864 31.0477 32.59352 372 0 26.40440 35201 75.16759 625 0 55.05155 81181 1080
Maroondah 6.818822 [km^2] 17.95823 [km^2] 4.761475e+00 [km^2] 1.8203022 9300.873 [km^2] 0.04454787 0.7314 0.0025 0.04899716 60.7633 32.33798 359 0 19.92773 44109 80.15103 428 0 48.45033 109326 1364
Melbourne 14.356652 [km^2] 17.86318 [km^2] 6.926089e+00 [km^2] 2.4081664 18792.858 [km^2] 0.02842200 1.7093 0.0005 0.10207796 37.2044 57.44538 1923 0 114.86270 75196 103.19862 3026 0 191.17421 135087 1309
Melton 5.715405 [km^2] 29.72466 [km^2] 2.024515e-02 [km^2] 2.4611315 8138.737 [km^2] 0.32922640 26.9598 0.0044 1.69415942 468.8184 32.39115 265 0 20.51866 46125 93.75000 1145 0 63.94201 133500 1424
Monash 10.555374 [km^2] 25.27145 [km^2] 5.442147e+00 [km^2] 3.6498928 22715.165 [km^2] 0.03696231 1.4265 0.0022 0.05004829 79.5429 32.43076 314 0 21.43371 69791 83.01301 554 0 50.41820 178644 2152
Moonee Valley 10.809779 [km^2] 12.87149 [km^2] 8.271961e+00 [km^2] 1.0397201 16139.000 [km^2] 0.02859806 3.3549 0.0013 0.08931930 42.6969 33.03282 789 0 29.94060 49318 76.76356 1490 0 63.80461 114608 1493
Moreland 10.956583 [km^2] 14.61052 [km^2] 7.987038e+00 [km^2] 1.2537250 22745.867 [km^2] 0.02417240 1.2339 0.0011 0.03794086 50.1819 33.70424 443 0 26.29540 69970 77.52601 678 0 53.04389 160944 2076
Mornington Peninsula 5.139249 [km^2] 15.67443 [km^2] 4.622905e-02 [km^2] 2.7640222 16198.913 [km^2] 0.21981129 16.3563 0.0013 0.89059119 692.8452 27.94004 552 0 22.38468 88067 48.63515 763 0 48.18917 153298 3152
Nillumbik 8.854466 [km^2] 33.27975 [km^2] 1.347111e-02 [km^2] 5.0391171 6375.216 [km^2] 0.56818264 25.2917 0.0066 1.83142826 409.0915 29.61250 72 0 18.19386 21321 83.19306 232 0 53.14318 59899 720
Port Phillip 9.677651 [km^2] 13.74884 [km^2] 5.349321e+00 [km^2] 1.8006317 15087.459 [km^2] 0.01312277 0.9645 0.0010 0.03606303 20.4584 36.37652 480 0 38.03437 56711 63.38101 688 0 59.99859 98811 1559
Stonnington 10.419494 [km^2] 14.88669 [km^2] 6.451218e+00 [km^2] 2.5647733 16441.961 [km^2] 0.01619544 0.2950 0.0009 0.01579035 25.5564 33.82890 500 0 37.04214 53382 64.96071 709 0 60.54401 102508 1578
Whitehorse 8.641114 [km^2] 14.20004 [km^2] 6.388966e+00 [km^2] 1.3954791 16763.762 [km^2] 0.03274536 0.5782 0.0010 0.02690308 63.5260 33.50773 216 0 21.37085 65005 82.45670 488 0 50.13996 159966 1940
Whittlesea 9.506851 [km^2] 68.79601 [km^2] 4.269360e-03 [km^2] 4.3284891 20762.963 [km^2] 0.19466584 23.6587 0.0043 1.16659428 425.1502 31.92674 424 0 22.84269 69728 89.04808 783 0 57.96041 194481 2184
Wyndham 8.512608 [km^2] 59.31198 [km^2] 1.129885e-01 [km^2] 5.7746505 19170.394 [km^2] 0.21506048 64.7456 0.0064 2.14269521 484.3162 33.21581 279 0 20.41285 74802 95.77575 1070 0 60.70830 215687 2252
Yarra 16.178230 [km^2] 17.87735 [km^2] 1.232576e+01 [km^2] 1.0529407 22067.106 [km^2] 0.01418094 0.6306 0.0007 0.02720264 19.3428 32.16642 576 0 44.98409 43875 63.21701 841 0 80.75648 86228 1364
Yarra Ranges 15.047158 [km^2] 78.50382 [km^2] 4.113114e-04 [km^2] 12.0433254 27190.214 [km^2] 1.34714073 195.6199 0.0021 8.66869834 2434.2833 31.60764 279 0 20.20072 57115 80.38129 448 0 50.85072 145249 1807 
df_mesh_lockdown_summary  %>%
  write_csv('output/lockdown_greenspace_mesh_detail.csv')

df_lga_summary %>%
  write_csv('output/lockdown_greenspace_lga_summary.csv')

SA2 level summary

(first 10 rows)

df_mesh_lockdown_summary  %>%
  group_by(SA2_MAIN16) %>%
  select(-starts_with('mc'), -State) %>%
  mutate( area = units::set_units( area, 'km^2') ) %>%
  summarise(across(.cols=where(is_numeric),
                   .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum),
                   .names = "{col}.{fn}"), number_mesh_blocks = n(),
            .groups='drop') %>%
  { . } -> df_sa2_summary
Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated
df_sa2_summary %>%
  head(10) %>%
gt::gt()
SA2_MAIN16 area.mean area.max area.min area.sd area.total AREA_ALBERS_SQKM.mean AREA_ALBERS_SQKM.max AREA_ALBERS_SQKM.min AREA_ALBERS_SQKM.sd AREA_ALBERS_SQKM.total Dwelling.mean Dwelling.max Dwelling.min Dwelling.sd Dwelling.total Person.mean Person.max Person.min Person.sd Person.total number_mesh_blocks
9.879583 [km^2] 9.879583 [km^2] 9.879583 [km^2] NA 9.879583 [km^2] 0.00700000 0.0070 0.0070 NA 0.0070 0.000000 0 0 NA 0 0.00000 0 0 NA 0 1
204011061 48.265157 [km^2] 77.934745 [km^2] 9.555915 [km^2] 23.6350021 723.977356 [km^2] 57.08476000 195.6199 0.0977 66.699794323 856.2714 8.333333 51 0 14.73900 125 12.40000 62 0 19.82351 186 15
206011105 11.203717 [km^2] 13.863971 [km^2] 10.181837 [km^2] 0.8583688 4055.745566 [km^2] 0.01416961 0.0835 0.0015 0.010364614 5.1294 33.660221 177 0 25.07734 12185 69.63260 345 0 48.40269 25207 362
206011106 12.735875 [km^2] 14.347035 [km^2] 10.897082 [km^2] 0.9840127 1884.909518 [km^2] 0.01468378 0.0486 0.0017 0.009396302 2.1732 38.898649 443 0 53.95674 5757 75.32432 678 0 80.64369 11148 148
206011107 11.560133 [km^2] 12.149738 [km^2] 10.766638 [km^2] 0.3104852 2335.146840 [km^2] 0.01564604 0.0531 0.0015 0.009148918 3.1605 32.693069 217 0 23.66295 6604 66.44059 385 0 47.55587 13421 202
206011108 10.413846 [km^2] 11.687802 [km^2] 7.987038 [km^2] 0.6994373 3322.016851 [km^2] 0.02133636 0.0946 0.0025 0.012247972 6.8063 33.595611 241 0 24.14948 10717 80.66458 394 0 52.86101 25732 319
206011109 10.694035 [km^2] 11.885915 [km^2] 9.063069 [km^2] 0.6690553 1229.813980 [km^2] 0.02557130 0.0771 0.0011 0.012561769 2.9407 32.495652 72 0 20.70570 3737 84.51304 217 0 55.67644 9719 115
206021110 13.923714 [km^2] 15.680964 [km^2] 11.967993 [km^2] 1.1507329 1851.853983 [km^2] 0.02213233 0.1003 0.0046 0.014736543 2.9436 29.511278 75 0 18.51888 3925 66.99248 193 0 43.65004 8910 133
206021111 14.064557 [km^2] 16.391927 [km^2] 12.154639 [km^2] 0.8867574 4697.561920 [km^2] 0.01807874 0.1447 0.0017 0.014407097 6.0383 31.610778 134 0 20.14955 10558 71.78144 248 0 46.17342 23975 334
206021112 12.166816 [km^2] 15.621660 [km^2] 10.346722 [km^2] 1.1697265 3212.039474 [km^2] 0.01874167 0.1857 0.0026 0.015694484 4.9478 32.363636 98 0 17.34695 8544 69.29545 257 0 41.23322 18294 264 
df_sa2_summary %>%
  write_csv('output/lockdown_greenspace_sa2_summary.csv')

SA1 level summary

(first 10 rows)

df_mesh_lockdown_summary  %>%
  group_by(SA1_MAIN16) %>%
  select(-starts_with('mc'), -State) %>%
  mutate( area = units::set_units( area, 'km^2') ) %>%
  summarise(across(.cols=where(is_numeric),
                   .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum),
                   .names = "{col}.{fn}"), number_mesh_blocks = n(),
            .groups='drop') %>%
  { . } -> df_sa1_summary
Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated
df_sa1_summary %>%
  head(10) %>%
  gt::gt()
SA1_MAIN16 area.mean area.max area.min area.sd area.total AREA_ALBERS_SQKM.mean AREA_ALBERS_SQKM.max AREA_ALBERS_SQKM.min AREA_ALBERS_SQKM.sd AREA_ALBERS_SQKM.total Dwelling.mean Dwelling.max Dwelling.min Dwelling.sd Dwelling.total Person.mean Person.max Person.min Person.sd Person.total number_mesh_blocks
5.867353 [km^2] 9.226164 [km^2] 1.117239 [km^2] 2.14084864 129.08178 [km^2] 0.01138182 0.0411 0.0020 0.008972691 0.2504 2.181818 48 0 10.233634 48 3.772727 83 0 17.695659 83 22
20401106101 48.265157 [km^2] 77.934745 [km^2] 9.555915 [km^2] 23.63500207 723.97736 [km^2] 57.08476000 195.6199 0.0977 66.699794323 856.2714 8.333333 51 0 14.738999 125 12.400000 62 0 19.823507 186 15
20601110501 10.761375 [km^2] 10.796213 [km^2] 10.726537 [km^2] 0.04926838 21.52275 [km^2] 0.02050000 0.0229 0.0181 0.003394113 0.0410 54.500000 59 50 6.363961 109 109.500000 115 104 7.778175 219 2
20601110502 10.397864 [km^2] 10.504986 [km^2] 10.327990 [km^2] 0.06129901 93.58077 [km^2] 0.01355556 0.0214 0.0050 0.005477707 0.1220 28.222222 40 8 10.353475 254 65.555556 106 8 30.745370 590 9
20601110503 10.643974 [km^2] 10.732194 [km^2] 10.564890 [km^2] 0.06359407 53.21987 [km^2] 0.01244000 0.0174 0.0096 0.002926260 0.0622 38.000000 53 27 9.643651 190 89.600000 154 69 36.239481 448 5
20601110504 11.213852 [km^2] 11.441371 [km^2] 11.037630 [km^2] 0.15975109 56.06926 [km^2] 0.01194000 0.0226 0.0037 0.008214499 0.0597 32.000000 52 7 18.881208 160 70.600000 138 15 49.812649 353 5
20601110505 10.736944 [km^2] 10.902372 [km^2] 10.589545 [km^2] 0.12858726 42.94778 [km^2] 0.01712500 0.0218 0.0121 0.004618351 0.0685 40.500000 50 30 9.146948 162 89.000000 113 60 21.924112 356 4
20601110506 10.382045 [km^2] 10.540552 [km^2] 10.283669 [km^2] 0.09766240 62.29227 [km^2] 0.01331667 0.0188 0.0073 0.004508843 0.0799 32.833333 51 25 9.579492 197 74.166667 92 53 16.351351 445 6
20601110507 10.706422 [km^2] 10.966954 [km^2] 10.479501 [km^2] 0.18201363 64.23853 [km^2] 0.01613333 0.0236 0.0089 0.005627314 0.0968 39.000000 59 3 19.544820 234 84.166667 130 5 45.595687 505 6
20601110508 11.406475 [km^2] 11.671566 [km^2] 11.145402 [km^2] 0.21835673 57.03238 [km^2] 0.01250000 0.0165 0.0086 0.003248846 0.0625 39.800000 53 23 10.825895 199 75.800000 86 54 13.084342 379 5 
  df_sa1_summary %>%
  write_csv('output/lockdown_greenspace_sa1_summary.csv')

Map

map_mesh %>%
  inner_join(df_mesh_lockdown_summary, by = "MB_CODE16" ) %>%
  mutate( area = units::set_units( area, 'km^2') %>% as.numeric() ) %>%
  ggplot() +
  geom_sf( aes( fill = area ),lwd=0 ) +
  scale_fill_viridis_c(option = "plasma") +
  ggtitle('Km^2 greenspace access within 5km of meshblock')

Version Author Date
b83cc3d Dennis Wollersheim 2020-08-05
1b1df89 Dennis Wollersheim 2020-08-05

Green Map

map_mesh %>%
  inner_join(df_mesh_lockdown_summary, by = "MB_CODE16" ) %>%
  filter( MB_CATEGORY_NAME_2016 == 'Parkland') %>%
  { . } -> df_parks

map_mesh %>%
  inner_join(df_mesh_lockdown_summary, by = "MB_CODE16" ) %>%
  filter( MB_CATEGORY_NAME_2016 != 'Parkland') %>%
  mutate( area = units::set_units( area, 'km^2') %>% as.numeric() ) %>%
  ggplot() +
  geom_sf( aes( fill = area ),lwd=0 ) +
  geom_sf(data=df_parks, fill="green") +
  scale_fill_viridis_c(option = "plasma") +
  ggtitle('Km^2 greenspace access within 5km of meshblock. Green is parks')

Version Author Date
b83cc3d Dennis Wollersheim 2020-08-05
a69a370 Dennis Wollersheim 2020-08-05
1b1df89 Dennis Wollersheim 2020-08-05

Interactive Map

map_levels = c('Park','0-5', '5-10', '10-15','15-20','>20')

map_colors = c(
               RColorBrewer::brewer.pal(3,'Greens')[1],
               RColorBrewer::brewer.pal(5,'Blues'))

map_palette = colorFactor(map_colors, levels=map_levels)

map_mesh %>%
  inner_join(df_mesh_lockdown_summary, by = "MB_CODE16" ) %>%
  mutate( area = units::set_units( area, 'km^2') %>% as.numeric() ) %>%
  mutate( area=ifelse( MB_CATEGORY_NAME_2016 == 'Parkland', -1, area )) %>%
  mutate( area_factor = cut( area, breaks= c(-2,0, 5,10,15,20,99),
                       labels= map_levels )) %>%
  { . } -> df_leaflet


df_leaflet %>%
  leaflet() %>%
  addPolygons(stroke = FALSE, smoothFactor = 0.2, fillOpacity = 1,
              color = ~map_palette(area_factor)) %>%
  addLegend("bottomright", pal = map_palette, values = ~area_factor,
            title = "Total parks within 5km circle",
            opacity = 1
  )
Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] rvest_0.3.5        xml2_1.3.2         leaflet_2.0.3.9000 workflowr_1.6.2   
 [5] cleangeo_0.2-3     maptools_1.0-1     rgeos_0.5-3        sp_1.4-2          
 [9] geohash_0.4.0      osrm_3.3.3         sf_0.9-3           janitor_2.0.1     
[13] rmarkdown_2.1      lubridate_1.7.8    geosphere_1.5-10   forcats_0.5.0     
[17] stringr_1.4.0      dplyr_1.0.0        purrr_0.3.4        readr_1.3.1       
[21] tidyr_1.1.0        tibble_3.0.3       ggplot2_3.3.2      tidyverse_1.3.0   
[25] drake_7.12.4       dotenv_1.0.2       conflicted_1.0.4   nvimcom_0.9-99    

loaded via a namespace (and not attached):
 [1] nlme_3.1-147       fs_1.4.1           RColorBrewer_1.1-2 filelock_1.0.2    
 [5] progress_1.2.2     httr_1.4.1         rprojroot_1.3-2    tools_4.0.2       
 [9] backports_1.1.8    R6_2.4.1           KernSmooth_2.23-17 DBI_1.1.0         
[13] colorspace_1.4-1   withr_2.2.0        tidyselect_1.1.0   prettyunits_1.1.1 
[17] compiler_4.0.2     git2r_0.27.1       Xmisc_0.2.1        cli_2.0.2         
[21] gt_0.2.1           labeling_0.3       sass_0.2.0         checkmate_2.0.0   
[25] scales_1.1.1       classInt_0.4-3     digest_0.6.25      foreign_0.8-79    
[29] txtq_0.2.3         pkgconfig_2.0.3    htmltools_0.5.0    dbplyr_1.4.3      
[33] htmlwidgets_1.5.1  rlang_0.4.7        readxl_1.3.1       rstudioapi_0.11   
[37] farver_2.0.3       generics_0.0.2     jsonlite_1.7.0     crosstalk_1.1.0.1 
[41] magrittr_1.5       Rcpp_1.0.5         munsell_0.5.0      fansi_0.4.1       
[45] lifecycle_0.2.0    stringi_1.4.6      whisker_0.4        yaml_2.2.1        
[49] snakecase_0.11.0   storr_1.2.1        grid_4.0.2         parallel_4.0.2    
[53] promises_1.1.1     crayon_1.3.4       lattice_0.20-41    haven_2.2.0       
[57] hms_0.5.3          knitr_1.28         pillar_1.4.6       igraph_1.2.5      
[61] base64url_1.4      reprex_0.3.0       glue_1.4.1         evaluate_0.14     
[65] modelr_0.1.7       vctrs_0.3.2        httpuv_1.5.4       cellranger_1.1.0  
[69] gtable_0.3.0       assertthat_0.2.1   xfun_0.16          broom_0.5.6       
[73] e1071_1.7-3        later_1.1.0.1      viridisLite_0.3.0  class_7.3-17      
[77] memoise_1.1.0      units_0.6-6        ellipsis_0.3.1